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docs: audit-driven URL fixes (workspace phase) #70

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6 changes: 3 additions & 3 deletions CITATIONS.md
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# Citations & References

The bibtex entries for **PyAutoGalaxy** and its affiliated software packages can be found
[here](https://github.com/Jammy2211/PyAutoGalaxy/blob/main/files/citations.bib), with example text for citing **PyAutoGalaxy**
in [.tex format here](https://github.com/Jammy2211/PyAutoGalaxy/blob/main/files/citations.tex) format here and
[.md format here](https://github.com/Jammy2211/PyAutoGalaxy/blob/main/files/citations.md).
[here](https://github.com/PyAutoLabs/PyAutoGalaxy/blob/main/files/citations.bib), with example text for citing **PyAutoGalaxy**
in [.tex format here](https://github.com/PyAutoLabs/PyAutoGalaxy/blob/main/files/citations.tex) format here and
[.md format here](https://github.com/PyAutoLabs/PyAutoGalaxy/blob/main/files/citations.md).

As shown in the examples, we would greatly appreciate it if you mention **PyAutoGalaxy** by name and include a link to
our GitHub page!
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4 changes: 2 additions & 2 deletions CODE_OF_CONDUCT.md
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## License

This code of conduct has been adapted from [*NUMFOCUS code of conduct*](https://github.com/numfocus/numfocus/blob/main/manual/numfocus-coc.md#the-short-version),
which is adapted from numerous sources, including the [*Geek Feminism wiki, created by the Ada Initiative and other volunteers, which is under a Creative Commons Zero license*](http://geekfeminism.wikia.com/wiki/Conference_anti-harassment/Policy), the [*Contributor Covenant version 1.2.0*](http://contributor-covenant.org/version/1/2/0/), the [*Bokeh Code of Conduct*](https://github.com/bokeh/bokeh/blob/main/CODE_OF_CONDUCT.md), the [*SciPy Code of Conduct*](https://github.com/jupyter/governance/blob/main/conduct/enforcement.md), the [*Carpentries Code of Conduct*](https://docs.carpentries.org/topic_folders/policies/code-of-conduct.html#enforcement-manual), and the [*NeurIPS Code of Conduct*](https://neurips.cc/public/CodeOfConduct).
This code of conduct has been adapted from [*NUMFOCUS code of conduct*](https://numfocus.org/code-of-conduct),
which is adapted from numerous sources, including the [*Geek Feminism wiki, created by the Ada Initiative and other volunteers, which is under a Creative Commons Zero license*](http://geekfeminism.wikia.com/wiki/Conference_anti-harassment/Policy), the [*Contributor Covenant version 1.2.0*](http://contributor-covenant.org/version/1/2/0/), the [*Bokeh Code of Conduct*](https://github.com/bokeh/bokeh/blob/main/docs/CODE_OF_CONDUCT.md), the [*SciPy Code of Conduct*](https://github.com/jupyter/governance/blob/main/conduct/enforcement.md), the [*Carpentries Code of Conduct*](https://docs.carpentries.org/topic_folders/policies/code-of-conduct.html#enforcement-manual), and the [*NeurIPS Code of Conduct*](https://neurips.cc/public/CodeOfConduct).

**PyAutoGalaxy Code of Conduct is licensed under the [Creative Commons Attribution 3.0 Unported License](https://creativecommons.org/licenses/by/3.0/).**
8 changes: 4 additions & 4 deletions README.md
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[Installation Guide](https://pyautogalaxy.readthedocs.io/en/latest/installation/overview.html) |
[readthedocs](https://pyautogalaxy.readthedocs.io/en/latest/index.html) |
[Introduction on Colab](https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.14.2/start_here.ipynb) |
[Introduction on Colab](https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.14.2/notebooks/imaging/start_here.ipynb) |
[HowToGalaxy](https://github.com/PyAutoLabs/HowToGalaxy)

Welcome to the **PyAutoGalaxy** Workspace!
Expand All @@ -15,7 +15,7 @@ You can get set up on your personal computer by following the installation guide
our [readthedocs](https://pyautogalaxy.readthedocs.io/).

Alternatively, you can try **PyAutoGalaxy** out in a web browser by going to
the [autogalaxy workspace on Colab](https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.14.2/start_here.ipynb).
the [autogalaxy workspace on Colab](https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.14.2/notebooks/imaging/start_here.ipynb).

## New Users

Expand All @@ -24,7 +24,7 @@ overview of **PyAutoGalaxy**'s core features and API.

This can be done via a web browser by going to the following Google Colab link:

https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.14.2/start_here.ipynb
https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.14.2/notebooks/imaging/start_here.ipynb

Then checkout the [new user starting guide](https://pyautogalaxy.readthedocs.io/en/latest/overview/overview_2_new_user_guide.html)
to navigate the workspace for your science case.
Expand Down Expand Up @@ -77,7 +77,7 @@ galaxy modeling and analysis, and helps troubleshoot problems.

Slack is invitation-only. If you'd like to join, please send an email requesting an invite.

For installation issues, bug reports, or feature requests, please raise an issue on the [GitHub issues page](https://github.com/Jammy2211/PyAutoGalaxy/issues).
For installation issues, bug reports, or feature requests, please raise an issue on the [GitHub issues page](https://github.com/PyAutoLabs/PyAutoGalaxy/issues).

## Contribution

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Expand Up @@ -118,10 +118,10 @@ cd $HOME/PyAuto
Clone all required repositories:

```
git clone https://github.com/rhayes777/PyAutoConf
git clone https://github.com/rhayes777/PyAutoFit
git clone https://github.com/Jammy2211/PyAutoArray
git clone https://github.com/Jammy2211/PyAutoGalaxy
git clone https://github.com/PyAutoLabs/PyAutoConf
git clone https://github.com/PyAutoLabs/PyAutoFit
git clone https://github.com/PyAutoLabs/PyAutoArray
git clone https://github.com/PyAutoLabs/PyAutoGalaxy
```

## 9. Install Python Dependencies
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"\n",
"The following example notebooks show how to compose and fit these models:\n",
"\n",
"https://github.com/Jammy2211/autogalaxy_workspace/blob/release/notebooks/modeling/imaging/features/multi_gaussian_expansion.ipynb\n",
"https://github.com/Jammy2211/autogalaxy_workspace/blob/release/notebooks/modeling/imaging/features/shapelets.ipynb\n",
"https://github.com/PyAutoLabs/autogalaxy_workspace/blob/main/notebooks/imaging/features/multi_gaussian_expansion/modeling.ipynb\n",
"https://github.com/PyAutoLabs/autogalaxy_workspace/blob/main/notebooks/imaging/features/shapelets/modeling.ipynb\n",
"\n",
"__Model Linking (Advanced)__\n",
"\n",
"When performing non-linear search chaining, the inferred model of one phase can be linked to the model.\n",
"\n",
"The following example notebooks show how to compose and fit these models:\n",
"\n",
"https://github.com/Jammy2211/autogalaxy_workspace/blob/release/notebooks/imaging/advanced/guides/modeling/chaining.ipynb\n",
"https://github.com/PyAutoLabs/autogalaxy_workspace/blob/main/notebooks/imaging/advanced/guides/modeling/chaining.ipynb\n",
"\n",
"__Across Datasets (Advanced)__\n",
"\n",
Expand All @@ -381,7 +381,7 @@
"\n",
"The following example notebooks show how to compose and fit these models:\n",
"\n",
"https://github.com/Jammy2211/autogalaxy_workspace/blob/release/notebooks/multi/modeling/start_here.ipynb\n",
"https://github.com/PyAutoLabs/autogalaxy_workspace/blob/main/notebooks/multi/start_here.ipynb\n",
"\n",
"__Relations (Advanced)__\n",
"\n",
Expand All @@ -390,7 +390,7 @@
"\n",
"The following example notebooks show how to compose and fit these models:\n",
"\n",
"https://github.com/Jammy2211/autogalaxy_workspace/blob/release/notebooks/multi/modeling/features/wavelength_dependence.ipynb\n",
"https://github.com/PyAutoLabs/autogalaxy_workspace/blob/main/notebooks/multi/features/wavelength_dependence/modeling.ipynb\n",
"\n",
"__PyAutoFit API__\n",
"\n",
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"\n",
"The preprocess module is found here:\n",
"\n",
"https://github.com/Jammy2211/PyAutoArray/blob/main/autoarray/dataset/preprocess.py\n",
"https://github.com/PyAutoLabs/PyAutoArray/blob/main/autoarray/dataset/preprocess.py\n",
"\n",
"Functions related to background subtraction are:\n",
"\n",
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"from the data you currently have (if it is not already RMS values including the Poisson noise contribution and\n",
"background sky contribution).\n",
"\n",
"https://github.com/Jammy2211/PyAutoArray/blob/main/autoarray/dataset/preprocess.py\n",
"https://github.com/PyAutoLabs/PyAutoArray/blob/main/autoarray/dataset/preprocess.py\n",
"\n",
"Functions related to the noise map are:\n",
"\n",
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"\n",
"The preprocess module contains functions for converting an even-sized PSF to an odd-sized PSF.\n",
"\n",
"https://github.com/Jammy2211/PyAutoArray/blob/main/autoarray/dataset/preprocess.py\n",
"https://github.com/PyAutoLabs/PyAutoArray/blob/main/autoarray/dataset/preprocess.py\n",
"\n",
"- `psf_with_odd_dimensions_from`\n",
"\n",
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"To fit a galaxy model to data, the likelihood function illustrated in this tutorial is sampled using a\n",
"non-linear search algorithm.\n",
"\n",
"The default sampler is the nested sampling algorithm `nautilus` (https://github.com/joshspeagle/nautilus)\n",
"The default sampler is the nested sampling algorithm `nautilus` (https://github.com/johannesulf/nautilus)\n",
"multiple MCMC and optimization algorithms are supported.\n",
"\n",
"For linear light profiles, the reduced number of free parameters (e.g. the `intensity` values are solved for\n",
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"To fit a galaxy model to data, the likelihood function illustrated in this tutorial is sampled using a\n",
"non-linear search algorithm.\n",
"\n",
"The default sampler is the nested sampling algorithm `nautilus` (https://github.com/joshspeagle/nautilus)\n",
"The default sampler is the nested sampling algorithm `nautilus` (https://github.com/johannesulf/nautilus)\n",
"multiple MCMC and optimization algorithms are supported.\n",
"\n",
"__Wrap Up__\n",
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"To fit a galaxy model to data, the likelihood function illustrated in this tutorial is sampled using a\n",
"non-linear search algorithm.\n",
"\n",
"The default sampler is the nested sampling algorithm `nautilus` (https://github.com/joshspeagle/nautilus)\n",
"The default sampler is the nested sampling algorithm `nautilus` (https://github.com/johannesulf/nautilus)\n",
"multiple MCMC and optimization algorithms are supported.\n",
"\n",
"__Log Likelihood Function: Pixelization With Light Profile__\n",
Expand Down
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"To fit a galaxy model to data, the likelihood function illustrated in this tutorial is sampled using a\n",
"non-linear search algorithm.\n",
"\n",
"The default sampler is the nested sampling algorithm `nautilus` (https://github.com/joshspeagle/nautilus)\n",
"The default sampler is the nested sampling algorithm `nautilus` (https://github.com/johannesulf/nautilus)\n",
"multiple MCMC and optimization algorithms are supported.\n",
"\n",
"__Wrap Up__\n",
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"To fit a galaxy model to data, the likelihood function illustrated in this tutorial is sampled using a\n",
"non-linear search algorithm.\n",
"\n",
"The default sampler is the nested sampling algorithm `nautilus` (https://github.com/joshspeagle/nautilus)\n",
"The default sampler is the nested sampling algorithm `nautilus` (https://github.com/johannesulf/nautilus)\n",
"multiple MCMC and optimization algorithms are supported.\n",
"\n",
"__Log Likelihood Function: Pixelization With Light Profile__\n",
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"A high-resolution `uv_wavelengths` file for ALMA is available in a separate repository that hosts large files which\n",
"are too big to include in the main `autogalaxy_workspace` repository:\n",
"\n",
"https://github.com/Jammy2211/autolens_workspace_large_files\n",
"https://github.com/PyAutoLabs/autolens_workspace_large_files\n",
"\n",
"After downloading the file, place it in the directory:\n",
"\n",
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"A high-resolution `uv_wavelengths` file for ALMA is available in a separate repository that hosts large files which\n",
"are too big to include in the main `autogalaxy_workspace` repository:\n",
"\n",
"https://github.com/Jammy2211/autolens_workspace_large_files\n",
"https://github.com/PyAutoLabs/autolens_workspace_large_files\n",
"\n",
"After downloading the file, place it in the directory:\n",
"\n",
Expand Down
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"To fit a galaxy model to data, the likelihood function illustrated in this tutorial is sampled using a\n",
"non-linear search algorithm.\n",
"\n",
"The default sampler is the nested sampling algorithm `nautilus` (https://github.com/joshspeagle/nautilus)\n",
"The default sampler is the nested sampling algorithm `nautilus` (https://github.com/johannesulf/nautilus)\n",
"multiple MCMC and optimization algorithms are supported.\n",
"\n",
"__Wrap Up__\n",
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Expand Up @@ -118,10 +118,10 @@ cd $HOME/PyAuto
Clone all required repositories:

```
git clone https://github.com/rhayes777/PyAutoConf
git clone https://github.com/rhayes777/PyAutoFit
git clone https://github.com/Jammy2211/PyAutoArray
git clone https://github.com/Jammy2211/PyAutoGalaxy
git clone https://github.com/PyAutoLabs/PyAutoConf
git clone https://github.com/PyAutoLabs/PyAutoFit
git clone https://github.com/PyAutoLabs/PyAutoArray
git clone https://github.com/PyAutoLabs/PyAutoGalaxy
```

## 9. Install Python Dependencies
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The following example notebooks show how to compose and fit these models:

https://github.com/Jammy2211/autogalaxy_workspace/blob/release/notebooks/modeling/imaging/features/multi_gaussian_expansion.ipynb
https://github.com/Jammy2211/autogalaxy_workspace/blob/release/notebooks/modeling/imaging/features/shapelets.ipynb
https://github.com/PyAutoLabs/autogalaxy_workspace/blob/main/notebooks/imaging/features/multi_gaussian_expansion/modeling.ipynb
https://github.com/PyAutoLabs/autogalaxy_workspace/blob/main/notebooks/imaging/features/shapelets/modeling.ipynb

__Model Linking (Advanced)__

When performing non-linear search chaining, the inferred model of one phase can be linked to the model.

The following example notebooks show how to compose and fit these models:

https://github.com/Jammy2211/autogalaxy_workspace/blob/release/notebooks/imaging/advanced/guides/modeling/chaining.ipynb
https://github.com/PyAutoLabs/autogalaxy_workspace/blob/main/notebooks/imaging/advanced/guides/modeling/chaining.ipynb

__Across Datasets (Advanced)__

Expand All @@ -255,7 +255,7 @@

The following example notebooks show how to compose and fit these models:

https://github.com/Jammy2211/autogalaxy_workspace/blob/release/notebooks/multi/modeling/start_here.ipynb
https://github.com/PyAutoLabs/autogalaxy_workspace/blob/main/notebooks/multi/start_here.ipynb

__Relations (Advanced)__

Expand All @@ -264,7 +264,7 @@

The following example notebooks show how to compose and fit these models:

https://github.com/Jammy2211/autogalaxy_workspace/blob/release/notebooks/multi/modeling/features/wavelength_dependence.ipynb
https://github.com/PyAutoLabs/autogalaxy_workspace/blob/main/notebooks/multi/features/wavelength_dependence/modeling.ipynb

__PyAutoFit API__

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The preprocess module is found here:

https://github.com/Jammy2211/PyAutoArray/blob/main/autoarray/dataset/preprocess.py
https://github.com/PyAutoLabs/PyAutoArray/blob/main/autoarray/dataset/preprocess.py

Functions related to background subtraction are:

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from the data you currently have (if it is not already RMS values including the Poisson noise contribution and
background sky contribution).

https://github.com/Jammy2211/PyAutoArray/blob/main/autoarray/dataset/preprocess.py
https://github.com/PyAutoLabs/PyAutoArray/blob/main/autoarray/dataset/preprocess.py

Functions related to the noise map are:

Expand Down
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The preprocess module contains functions for converting an even-sized PSF to an odd-sized PSF.

https://github.com/Jammy2211/PyAutoArray/blob/main/autoarray/dataset/preprocess.py
https://github.com/PyAutoLabs/PyAutoArray/blob/main/autoarray/dataset/preprocess.py

- `psf_with_odd_dimensions_from`

Expand Down
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To fit a galaxy model to data, the likelihood function illustrated in this tutorial is sampled using a
non-linear search algorithm.

The default sampler is the nested sampling algorithm `nautilus` (https://github.com/joshspeagle/nautilus)
The default sampler is the nested sampling algorithm `nautilus` (https://github.com/johannesulf/nautilus)
multiple MCMC and optimization algorithms are supported.

For linear light profiles, the reduced number of free parameters (e.g. the `intensity` values are solved for
Expand Down
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To fit a galaxy model to data, the likelihood function illustrated in this tutorial is sampled using a
non-linear search algorithm.

The default sampler is the nested sampling algorithm `nautilus` (https://github.com/joshspeagle/nautilus)
The default sampler is the nested sampling algorithm `nautilus` (https://github.com/johannesulf/nautilus)
multiple MCMC and optimization algorithms are supported.

__Wrap Up__
Expand Down
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To fit a galaxy model to data, the likelihood function illustrated in this tutorial is sampled using a
non-linear search algorithm.

The default sampler is the nested sampling algorithm `nautilus` (https://github.com/joshspeagle/nautilus)
The default sampler is the nested sampling algorithm `nautilus` (https://github.com/johannesulf/nautilus)
multiple MCMC and optimization algorithms are supported.

__Log Likelihood Function: Pixelization With Light Profile__
Expand Down
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To fit a galaxy model to data, the likelihood function illustrated in this tutorial is sampled using a
non-linear search algorithm.

The default sampler is the nested sampling algorithm `nautilus` (https://github.com/joshspeagle/nautilus)
The default sampler is the nested sampling algorithm `nautilus` (https://github.com/johannesulf/nautilus)
multiple MCMC and optimization algorithms are supported.

__Wrap Up__
Expand Down
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To fit a galaxy model to data, the likelihood function illustrated in this tutorial is sampled using a
non-linear search algorithm.

The default sampler is the nested sampling algorithm `nautilus` (https://github.com/joshspeagle/nautilus)
The default sampler is the nested sampling algorithm `nautilus` (https://github.com/johannesulf/nautilus)
multiple MCMC and optimization algorithms are supported.

__Log Likelihood Function: Pixelization With Light Profile__
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A high-resolution `uv_wavelengths` file for ALMA is available in a separate repository that hosts large files which
are too big to include in the main `autogalaxy_workspace` repository:

https://github.com/Jammy2211/autolens_workspace_large_files
https://github.com/PyAutoLabs/autolens_workspace_large_files

After downloading the file, place it in the directory:

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A high-resolution `uv_wavelengths` file for ALMA is available in a separate repository that hosts large files which
are too big to include in the main `autogalaxy_workspace` repository:

https://github.com/Jammy2211/autolens_workspace_large_files
https://github.com/PyAutoLabs/autolens_workspace_large_files

After downloading the file, place it in the directory:

Expand Down
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